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UF HemBank 1852 case study

UF HemBank 1852 case study#

Epigenomic analysis#

UF HemBank 1852 (20x coverage)#

  • Est. bases: 63Gb

  • Total CpGs: 28,983,095

  • Overlapping CpGs: 331,886

  • Est. sequencing time: >5000min

Hide code cell source 
import pandas as pd
import sys
sys.path.append('../')

from source.bokeh_plots import *
from source.data_visualization import *
output_notebook()

mount = '/mnt/e/'
input_path = mount + 'MethylScore_v2/Processed_Data/'

test_sample_name = 'uf_hembank_1852'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                     cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
Loading BokehJS ...
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(AML with NUP98-fusion)
uf_hembank_1852 High 0.786 AML with NUP98-fusion 0.83

UF HemBank 1852 (0.0001x coverage)#

  • Est. bases: ~300Kb

  • Total CpGs: 3248

  • Overlapping CpGs: 27

  • Est. sequencing time: <2min

test_sample_name = 'uf_hembank_1852_00001x'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                    cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(AML with mutated NPM1)
uf_hembank_1852_00001x High 0.676 AML with mutated NPM1 0.72

UF HemBank 1852 (0.001x coverage)#

  • Est. bases: ~3Mb

  • Total CpGs: 28865

  • Overlapping CpGs: 346

  • Est. sequencing time: 2.5min

test_sample_name = 'uf_hembank_1852_0001x'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                    cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(Otherwise-Normal Control)
uf_hembank_1852_0001x High 0.561 Otherwise-Normal Control 0.995

UF HemBank 1852 0.01x coverage#

  • Est. bases: ~30Mb

  • Total CpGs: 274,747

  • Overlapping CpGs: 2,571

  • Est. sequencing time: 6min

test_sample_name = 'uf_hembank_1852_001x'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                    cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(AML with NUP98-fusion)
uf_hembank_1852_001x High 0.755 AML with NUP98-fusion 0.934

UF HemBank 1852 0.1x coverage#

  • Est. bases: ~300Mb

  • Total CpGs: 2,606,667

  • Overlapping CpGs: 27,376

  • Est. sequencing time: 26min

test_sample_name = 'uf_hembank_1852_01x'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                    cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(AML with NUP98-fusion)
uf_hembank_1852_01x High 0.77 AML with NUP98-fusion 0.944

UF HemBank 1852 1x coverage#

  • Est. bases: ~3Gb

  • Total CpGs: 17,208,161

  • Overlapping CpGs: 190,119

  • Est. sequencing time: 143min

test_sample_name = 'uf_hembank_1852_1x'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                    cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(AML with NUP98-fusion)
uf_hembank_1852_1x High 0.698 AML with NUP98-fusion 0.944

UF HemBank 1852 (10x coverage)#

  • Est. bases: ~30Gb

  • Total CpGs: 28,875,278

  • Overlapping CpGs: 331,375

  • Est. sequencing time: 1575min

test_sample_name = 'uf_hembank_1852_10x'
df_nanopore = pd.read_pickle(input_path + test_sample_name + '_processed.pkl')

plot_linked_scatters(df_nanopore, table=False, test_sample=test_sample_name,
                     xaxis = "PaCMAP 1 of 2", yaxis = "PaCMAP 2 of 2",
                    cols=['WHO 2022 Diagnosis'])

df_nanopore.iloc[-1:,:][['AML Epigenomic Risk', 'AML Epigenomic Risk P(High Risk)',\
    'AL Epigenomic Phenotype', f'P({df_nanopore.iloc[-1:,:]["AL Epigenomic Phenotype"].item()})']]
AML Epigenomic Risk AML Epigenomic Risk P(High Risk) AL Epigenomic Phenotype P(AML with NUP98-fusion)
uf_hembank_1852_10x High 0.711 AML with NUP98-fusion 0.926

Genomic analysis#

Insertion upstream of NUP98 TSS#

Image Description

Reports#

Contents